OEE (Overall Equipment Effectiveness) – A Practical Guide

Overall equipment effectiveness (OEE)

In today’s competitive climate, even small improvements can lead to significant benefits to your bottom line. That is especially evident in the manufacturing industry where shaving off a few seconds on one production process or reducing the number of defects for just 1% can bring in tens of thousands of dollars every month.

One of the most common measurements used to optimize production is OEE (overall equipment effectiveness).

When used correctly, measuring OEE gives you a deep understanding of your production process which results in some major benefits:

  • squeeze every drop of performance from your machinery
  • reduce the number of defective products/parts
  • maximize workforce productivity
  • reduce asset repair costs by noticing problems early on
  • optimize your whole production process by eliminating wasteful actions
All of these improvements will ensure that you stay competitive on the market and give you the ability to maximize ROI from all assets that you add to your production line.

If those benefits sound appealing, continue reading this article and learn how you can take full advantage of OEE at your facility.


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Table of Content
  1. OEE definition
  2. How to calculate OEE
  3. What is a good OEE score
  4. OEE factors
  5. How to improve Overall Equipment Effectiveness Score
  6. Measure to improve

OEE definition

Overall Equipment Effectiveness (OEE) is the measure of an asset’s performance compared to its full potential. It quantifies the utilization of manufacturing resources – specifically physical assets, time, and materials – during production to indicate any gaps between actual and ideal performance.

The term OEE was first used by Japanese maintenance consultant and teacher Seiichi Nakajima in the 1960s. He is commonly referred to as the “Father of TPM” and he stated that continuous improvement of OEE is one of the main goals of TPM (Total Productive Maintenance).

Today, OEE has become a common key performance indicator (KPI) and manufacturing best practice for determining the portion of the manufacturing process that is truly productive.

OEE is most commonly determined based on three underlying metrics:

  • Availability (measures machine uptime)
  • Performance (measures system speed)
  • Quality (measures levels of defects)
The ability to calculate OEE is vital in any manufacturing process as it immediately shows up any losses. It offers valuable insights for systematic improvements. OEE remains the standard to date for eliminating waste and benchmarking production process with a view to continuously improving productivity.

To use it, you first need to know how to do the necessary calculations.

How to calculate OEE

There are two methods for calculating OEE:

  1. simple calculation
  2. advanced calculation
We will discuss both below.

Simple Overall Equipment Effectiveness calculation
The most straightforward way to calculate overall equipment effectiveness is to multiply the number of good parts (good count) produced with ideal cycle time, and then divide that number with planned production time.

Overall equipment effectiveness calculation

Example #1 – Plant “Nimble”

Let’s start with the simpler calculation of overall equipment effectiveness using the hypothetical example of a screw manufacturing plant called “Nimble”.

Let’s say that in a 6-hour shift (or 21600 seconds), Nimble produces 3600 units.

After they looked at their production process in more detail, plant managers at Nimble come to the conclusion that the fastest production cycle time in ideal conditions (no stops and production line runs at full designed capacity) is 4 seconds.

For the purpose of this example let’s also assume that, at the end of the shift, there were 100 defects.

This gives us the following numbers:

  • Planned production time is a 6-hour shift (21600 seconds)
  • Ideal cycle time for one piece is 4 seconds
  • The number of screws that passed quality control at the end of the shift is 3500
OEE = (Ideal cycle time x Good count) / Planned production time

OEE = (4 seconds x 3500 units) / 21600 seconds

OEE = 14000 / 21600 = 0.6481

OEE = 64.8%

Although this is an acceptable calculation of OEE, it doesn’t give us enough insight to know what we need to change if we want to improve it. For that, we need to use the advanced version of this calculation.

Advanced OEE calculation
The advanced OEE calculation takes into consideration 3 factors that have a big impact on OEE.

Advanced OEE calculation example

Every one of these factors first needs to be calculated separately. We will show you how to do that in the next example.

Example #2 – Plant “XYZ”

This time we will use a hypothetical example of a spoon manufacturing plant called “XYZ”.

1) Calculating Availability

OEE Availability calculation

Plant XYZ is scheduled to run for a 6-hour (360 minutes) shift with a 60-minute lunch break for the staff. However, during the shift, a critical piece of equipment breaks down. It takes maintenance 1 hour to get the line running again.

So the numbers are:

  • Planned production time is 300 minutes
  • Unexpected downtime is 60 minutes
Availability = Run time / Planned production time

Availability = 240 minutes / 300 minutes

Availability = 80%

2) Calculating Performance

OEE Performance calculation

Continuing from the example above, plant XYZ in ideal conditions produces 3600 spoons in 60 minutes, in other words, 1 spoon every second. Because of the lunch break and the downtime, the productive time for this particular shift was only 240 minutes. In this timeframe, they were able to produce 14100 units.

That gives us the following numbers:

  • Ideal cycle time is 1 second
  • Total count is 14100 units
  • Run time is 240 minutes or 14400 seconds
Performance = (Total count x Ideal cycle time) / Run time

Performance = (14100 units x 1 second) / 14400

Performance = 14100 / 14400

Performance = 97.9%

Notice how this calculation is based on total units produced. In other words, the performance calculation doesn’t care if the produced part was good or defective because that is the job of quality calculation which we will discuss next.

3) Calculating Quality

OEE Quality calculation

On closer examination, the quality control staff finds that 170 out of the 14 100 units produced are defective.

Quality = (Units produced – defective units) / (Units produced)

Quality = (14100 – 170) / 14 100

Quality = 98.8%

Now we have everything we need to calculate OEE.

4) Calculating OEE for the plant XYZ

Using the figures above, we get:

(Availability = 80%) * (Performance = 97.9%) * (Quality = 98.8%) = (OEE = 77.38%)

OEE = 0.8 x 0.979 x 0.988

OEE = 0.7738

OEE = 77.38%

In the end, we could conclude that the plant XYZ has a good score and that the first improvements they should make is to focus more on regular maintenance so they avoid unexpected failures and improve the availability score.

Of course, calculating this by measuring a single shift once can give misleading results. Gathering data over a longer period of time gives you a more objective measurement.

What is a good OEE score

As seen from the above examples, OEE scores are presented in percentages.

Achieving an OEE score of 100% means that the organization has attained perfect production. They are manufacturing only good products, within the fastest time possible, and with no downtime.

As a benchmarking resource, OEE helps plant owners compare their business’s performance to the industry’s best standards. It’s also a helpful tool for comparing the performance of different work shifts within the same organization.

According to Lean Production, here is how OEE percentage compares in the industry of discrete manufacturers:

  • 85% is highly sought-after and viewed as a world-class performance.
  • 60% is commonly seen among manufacturers and it implies that there is room for substantial improvement.
  • 40% is common among manufacturers that are just starting the process of tracking and improving their manufacturing performance. It is a low score, but can usually be quickly improved by closely examining each of the three loss factors.
In 2018., Sage Clarity conducted an OEE benchmark study on over 100 global manufacturing operations worldwide. The numbers match the ones from Lean Production.

OEE benchmark study

OEE Factors

To understand how to improve overall equipment effectiveness at your plant, you first need to have a deeper understanding of the main factors used in OEE calculations and the losses they measure.

Looking at these factors in isolation will help you understand which parts of your manufacturing process need and should be improved to eliminate all losses and maximize productive time.

OEE losses

Schedule loss is not taken into account for OEE calculations as there is no point in measuring OEE at times when production isn’t even supposed to run. Schedule loss is used for calculating TEEP (Total Effective Equipment Performance).

Let’s take a closer look at the OEE factors and associated losses.

#1) Availability
Availability is concerned with planned and unplanned stops during production. It is a measurement of uptime and it calculates the percentage of time that an asset actually operates during scheduled production. This metric shows up losses due to issues like:

These losses are called availability losses.

100% Availability means the process is running according to the planned production schedule and doesn’t experience any unplanned or planned stops.

#2) Performance
Performance is also referred to as process rate and it shows the speed at which the system runs compared to its designed (maximum possible) speed. Common problems that affect this metric are:

  • poor quality input material
  • old, weared-out equipment that isn’t able to run at designed capacity
  • process issues like minor jams (longer stops are often classified under equipment failure)
Losses due to wasted performance are called performance losses.

100% Performance represent a manufacturing process that is running at its maximum possible speed.

#3) Quality
In the context of OEE, Quality is a measure that defines what is the percentage of produced good parts (parts that meet the defined quality standard). This metric helps to establish the level of defects (scrap and parts that need rework) in the manufacturing process.  

As you can already guess by now, manufactured parts that do not meet defined quality standards represent quality losses.

100% Quality would mean you have achieved perfect production with zero defects.

How to improve Overall Equipment Effectiveness Score

The only way to improve OEE is to work on reducing the losses we just described. If we go a little deeper and try to categorize those losses, we will end up looking at what is known as Six big losses.

These losses represent the most common reasons for productivity loss in the manufacturing setting. You can see all of them on the picture below:

oee six big losses


How to reduce Availability losses?
To improve availability – the percentage of time a piece of equipment is functional – seek to address the conditions that cause unavailability in the first place like:

#1) Equipment failure

The contributing factors to equipment failure may vary but the effects largely remain the same: frustrating loss of productivity. In fact, downtime or unplanned stops due to equipment failure remain the largest single source of lost production time.

Fortunately, equipment failure is so disruptive that the cause of the malfunction is usually quickly visible thereby making it easy to identify and execute the required repairs. There are several strategies for minimizing equipment failure such as:

  • Performing regular maintenance – Equipment and tools should be inspected regularly and included on a preventive maintenance schedule. Even better, you can combine the advantages of automation through a CMMS with a proactive maintenance plan to minimize equipment failure.
  • Tracking downtime – It would be difficult to handle the issue of downtime without collecting equipment failure history and understanding the trends. This information allows the maintenance team to forecast and implement actions to prevent incessant asset failure. Again, CMMS is a valuable tool for gathering and analyzing the necessary data in this regard.
  • Categorizing reasons for loss – It’s vital to understand why equipment has shut down. So, there should be a system in place to capture reasons for every downtime event. If you want to take this a step further, you can perform Reliability Centered Maintenance analysis to identify failure modes of your assets and their maintenance needs.
With this information, the team can begin reducing downtime by attending to the reasons one after the other starting with the most common causes of downtime.

#2) Setup and adjustments

Setup and adjustment losses are categorized under planned stops. They happen during changeover or adjustment periods such as the time spent retooling a machine to adapt it for making a different product. It can also include the time when assets are shut down to carry out preventive maintenance (a.k.a. planned downtime). Although this is beneficial to production, it could be timed better to not interfere excessively with planned production time.

Some options for reducing this type of loss include:

  • Batch production – Instead of retooling a particular machine back and forth several times a day, it should be possible to closely predict how much of an item will be made during a period. Using that information, operators can produce the goods for storage in anticipation of orders. This will minimize the number of retooling adjustments required per day.
  • Single-Minute Exchange of Die (SMED) technique – this is also known as Quick Changeover and it is a lean technique that allows for rapid and efficient change from one process to another thereby saving time. For instance, switching from one manufacturing process to another for creating different products. The aim is to complete the switch within “single minute” or single-digit time frame, i.e. less than 10 minutes.
To get the benefits of SMED, each stage of the setup is analyzed to check how it can be improved to be faster, safer, to use simpler tools, and so on.

How to reduce Performance losses?
Performance losses can easily become a major cause of inefficiency in a production line. They are tricky to monitor because it’s not a case of a complete shutdown of the system. Rather, there is still production but not at the optimum expected rate.

Although they are difficult to completely eliminate, performance losses can be reduced significantly through the following:

#1) Idling/minor stops

Idling and minor stops are seemingly insignificant stops typically lasting for less than five minutes. However, if not quickly caught, they can cause production to slow down. Frequently repeated “minor” incidents such as a backed-up conveyor belt today or a jammed component tomorrow quickly add up over time and increase loss. Below are some practices to manage idling and minor stops.

  • Prompt notification and action – Rather than waiting for someone to notice the idling/minor stop then report it and wait for authorization to fix the problem, it would be better to set up a process where small deviations are quickly resolved. Operators can be empowered to find smaller issues and respond to them. This will reduce the risk of a bigger problem because of delaying the intervention.
  • Track patterns of performance loss – With careful observation, one may notice that incidents of minor stops are happening more frequently at a particular time. For instance, stops may increase after routine servicing, or during a particular shift, or after the asset has been in use for a period of time. It becomes easier to identify a pattern and take corrective action going forward.
  • Standardize processes – Minor stops can also be linked to human causes. Standardized work processes with all the needed information well documented and easily accessible will help ensure that all workers are complying with the same routine, and help to reduce the risk of mistakes.
As the organization makes progress in tracking problems and eliminating them, it is necessary to reflect these improvements in their standard procedures and then train relevant staff accordingly. This will prevent the same kind of problems from reoccurring.

#2) Reduced speed

Reduced speed, or slow cycle, occurs when equipment runs slower than it’s Ideal Cycle Time. Although it is highly unlikely that a machine will continue running at 100% maximum speed for extended periods of time, it’s still ideal to aim for levels that are very close.

Reduced speed may be caused by wear and tear, aging, poor maintenance, electrical problems, mishandling, etc.

The following actions can help address the issue of reduced speed:

  • Adequate equipment maintenance – Maintaining critical equipment when due and in line with its manufacturer’s recommendations will resolve a considerable proportion of mechanical issues and other related problems.
  • Continuous improvement – Frequently reviewing and evaluating the way operations are run with a focus on catching any areas of inefficiency and waste – then devising steps to eliminate them.
How to reduce Quality losses?
Defective products constitute a loss of revenue for the organization and could lead to expensive product liability claims if they happen to enter the market. Quality loss may occur as Process Defects or Reduced Yield.

#1) Process defects

Process defects (production rejects) may occur at any time during normal production. Tracking the causes of these defects and quickly resolving them is important for smooth operations.

Process defects can be checked mainly through monitoring the machines directly. Especially in the case of aging equipment, it’s not uncommon to find that a large number of defects in a plant are coming from aging equipment because the asset has obviously deteriorated over time. This can be managed with close monitoring and frequent maintenance but it can quickly become a never-ending cycle. At some point, it would be more economical to replace the machine entirely.

For newer equipment, the problems can usually be traced to settings that need to be fine-tuned and to operator errors.

#2) Reduced yield

Reduced yield (a.k.a. startup rejects) are easier to track because they occur in the period between startup and steady production. Typically, they are a result of setups, changeovers, and equipment warm up.

In such instances, the first set of products out of the machine will have some defects. Depending on the type of product, it may be reworked or have to be scrapped completely leading to excessive waste.

Take the following steps to lower startup rejects:

  • Reduce initial production – Instead of scrapping a large batch of products, try producing a small batch at startup and identifying any potential problems before progressing to full production capacity.
  • Reduce variation – Manufacturers can probably relate with the challenges of producing good quality products on one day but having poor products the very next day. Variation is the cause of this issue. By pinpointing the source of variation and solving it, there will be a more consistent outcome and more cost-effective operations. Two common sources of variation are inconsistent equipment settings and materials quality. Strict equipment settings and better materials testing and quality control will help reduce this challenge.

Measure to improve

If you want to accurately track the impact of implemented changes on your production process, you have to rely on numbers. It doesn’t matter if you are measuring MTTR, MTBF, OEE or something else, numbers are always here to give you objective information – if you review them in the proper context.

The best way to use OEE is to take a week or more to gather the baseline numbers for your system. Using the advanced OEE calculation you can estimate what needs to be improved. After you implement the first set of changes, measure the performance of the system for the same amount of time and review the data again. Adjust further changes according to your results. Rinse and repeat.

The theory is simple, but implementing this advice in the real word will take some practice. We hope this guide will help you in that regard.

If you have any questions about using CMMS to improve your OEE score or maintenance in general, leave a comment below or get in touch using our contact form.

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  • adnan akcora June 7, 2019, 9:59 am

    Thanks for good information

    What is diffrenece between OEE and mtbf also mttr .

    Which ismuch more effective .

    And how about Reliability ?


  • Abraham Milks December 10, 2019, 8:30 am

    This web site is known as a stroll-by way of for the entire information you wanted about this and didn’t know who to ask. Glimpse right here, and you’ll undoubtedly uncover it.

  • PHAM HUY KHA February 10, 2020, 10:30 pm

    how we can to created a OEE ?

    • Senko February 11, 2020, 1:38 am

      I’m not 100% sure what you are asking, could you elaborate a bit?

  • Aays August 18, 2020, 12:09 am

    for performance section:
    plant XYZ produces 3600 for 60 min
    then the planned production time is 240 min
    The total count should be (3600/60)*240 = 14400 right?
    How did you get 14100 ?

    I’ll try with my manufacturing and i get my performance as 100%. Can you help me?

    Thank you.

    • Senko August 18, 2020, 2:51 am


      In short, 3600 units in 60 min is in ideal conditions (what the machine manual would say the machine can do when it is brand new).

      We randomly selected a lower number of the total actual produced units that represents a more realistic scenario where conditions are not ideal. In other words, in that example, we imagine that wear and tear or poor quality of the input material slows down the production a tiny bit so the total number of produced units is less than theoretically possible.

      Maybe the text didn’t explain that point in the clearest way, I hope that this clears things up a bit.

  • Aays August 19, 2020, 1:57 am

    Okay, thank you for the explanation.
    I want to ask one question. I want calculate OEE for my machines. So, how can I randomly chooses any number for the total count. The calculation for performance is 100%. Is it okay? As I don’t randomly choose lower number as you do.

  • Senko August 21, 2020, 8:25 am

    You definitely should not take a random number 🙂 If possible, you should measure the actual output and that will be your total count. Then you can see if that actual output was the same as maximally possible output you defined earlier.

  • Sangeet September 25, 2020, 10:08 pm

    You need to calculate the downtime of a machine first on a daily basis.
    Downtime will include 4 losses of availability i.e. Shutdown loss, Production adjustment loss, equipment failure loss, and process failure loss.
    Then deduct these loss timing from planned production time( the result will run time). Now calculate availability as (run time/planned prod. time).
    Then calculate performance as {(ideal cycle time*total count)/run time}.
    At last, calculate Quality as (good count/total count).
    calculating OEE = Availability*Performance*quality

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